Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations1384617
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory105.6 MiB
Average record size in memory80.0 B

Variable types

Numeric8
Categorical2

Alerts

eval_set has constant value "train" Constant
order_dow has 324026 (23.4%) zeros Zeros
days_since_prior_order has 17044 (1.2%) zeros Zeros

Reproduction

Analysis started2024-11-01 21:07:35.426262
Analysis finished2024-11-01 21:08:03.001191
Duration27.57 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

order_id
Real number (ℝ)

Distinct131209
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1706297.6
Minimum1
Maximum3421070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 MiB
2024-11-01T17:08:03.113463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile170761
Q1843370
median1701880
Q32568023
95-th percentile3249514.2
Maximum3421070
Range3421069
Interquartile range (IQR)1724653

Descriptive statistics

Standard deviation989732.65
Coefficient of variation (CV)0.5800469
Kurtosis-1.2066256
Mean1706297.6
Median Absolute Deviation (MAD)861914
Skewness0.0063315648
Sum2.3625687 × 1012
Variance9.7957072 × 1011
MonotonicityIncreasing
2024-11-01T17:08:03.253655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1395075 80
 
< 0.1%
2813632 80
 
< 0.1%
949182 77
 
< 0.1%
2869702 76
 
< 0.1%
341238 76
 
< 0.1%
312611 75
 
< 0.1%
1465173 74
 
< 0.1%
1355077 74
 
< 0.1%
653280 72
 
< 0.1%
288915 72
 
< 0.1%
Other values (131199) 1383861
99.9%
ValueCountFrequency (%)
1 8
 
< 0.1%
36 8
 
< 0.1%
38 9
 
< 0.1%
96 7
 
< 0.1%
98 49
< 0.1%
112 11
 
< 0.1%
170 17
 
< 0.1%
218 5
 
< 0.1%
226 13
 
< 0.1%
349 11
 
< 0.1%
ValueCountFrequency (%)
3421070 3
 
< 0.1%
3421063 4
 
< 0.1%
3421058 8
 
< 0.1%
3421056 5
 
< 0.1%
3421049 6
 
< 0.1%
3421026 6
 
< 0.1%
3420998 28
< 0.1%
3420996 11
 
< 0.1%
3420979 6
 
< 0.1%
3420909 10
 
< 0.1%

product_id
Real number (ℝ)

Distinct39123
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25556.236
Minimum1
Maximum49688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 MiB
2024-11-01T17:08:03.385386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3397
Q113380
median25298
Q337940
95-th percentile47601
Maximum49688
Range49687
Interquartile range (IQR)24560

Descriptive statistics

Standard deviation14121.272
Coefficient of variation (CV)0.55255682
Kurtosis-1.1537944
Mean25556.236
Median Absolute Deviation (MAD)12122
Skewness-0.022354791
Sum3.5385598 × 1010
Variance1.9941034 × 108
MonotonicityNot monotonic
2024-11-01T17:08:03.516697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24852 18726
 
1.4%
13176 15480
 
1.1%
21137 10894
 
0.8%
21903 9784
 
0.7%
47626 8135
 
0.6%
47766 7409
 
0.5%
47209 7293
 
0.5%
16797 6494
 
0.5%
26209 6033
 
0.4%
27966 5546
 
0.4%
Other values (39113) 1288823
93.1%
ValueCountFrequency (%)
1 76
< 0.1%
2 4
 
< 0.1%
3 6
 
< 0.1%
4 22
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 13
 
< 0.1%
9 5
 
< 0.1%
10 119
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
49688 4
 
< 0.1%
49687 1
 
< 0.1%
49686 7
 
< 0.1%
49683 2413
0.2%
49682 5
 
< 0.1%
49681 8
 
< 0.1%
49680 46
 
< 0.1%
49679 4
 
< 0.1%
49678 21
 
< 0.1%
49677 8
 
< 0.1%

add_to_cart_order
Real number (ℝ)

Distinct80
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7580443
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 MiB
2024-11-01T17:08:03.634277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q312
95-th percentile23
Maximum80
Range79
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.4239365
Coefficient of variation (CV)0.84767058
Kurtosis4.1722265
Mean8.7580443
Median Absolute Deviation (MAD)4
Skewness1.6855488
Sum12126537
Variance55.114833
MonotonicityNot monotonic
2024-11-01T17:08:03.770779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 131209
 
9.5%
2 124364
 
9.0%
3 116996
 
8.4%
4 108963
 
7.9%
5 100745
 
7.3%
6 91850
 
6.6%
7 83142
 
6.0%
8 74601
 
5.4%
9 66618
 
4.8%
10 59401
 
4.3%
Other values (70) 426728
30.8%
ValueCountFrequency (%)
1 131209
9.5%
2 124364
9.0%
3 116996
8.4%
4 108963
7.9%
5 100745
7.3%
6 91850
6.6%
7 83142
6.0%
8 74601
5.4%
9 66618
4.8%
10 59401
4.3%
ValueCountFrequency (%)
80 2
 
< 0.1%
79 2
 
< 0.1%
78 2
 
< 0.1%
77 3
 
< 0.1%
76 5
< 0.1%
75 6
< 0.1%
74 8
< 0.1%
73 8
< 0.1%
72 10
< 0.1%
71 10
< 0.1%

reordered
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.6 MiB
1
828824 
0
555793 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1384617
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 828824
59.9%
0 555793
40.1%

Length

2024-11-01T17:08:03.903039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-01T17:08:03.986672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 828824
59.9%
0 555793
40.1%

Most occurring characters

ValueCountFrequency (%)
1 828824
59.9%
0 555793
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1384617
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 828824
59.9%
0 555793
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1384617
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 828824
59.9%
0 555793
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1384617
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 828824
59.9%
0 555793
40.1%

user_id
Real number (ℝ)

Distinct131209
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103112.78
Minimum1
Maximum206209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 MiB
2024-11-01T17:08:04.086387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10425
Q151732
median102933
Q3154959
95-th percentile195696
Maximum206209
Range206208
Interquartile range (IQR)103227

Descriptive statistics

Standard deviation59487.148
Coefficient of variation (CV)0.57691342
Kurtosis-1.2007212
Mean103112.78
Median Absolute Deviation (MAD)51608
Skewness-0.0003274701
Sum1.4277171 × 1011
Variance3.5387208 × 109
MonotonicityNot monotonic
2024-11-01T17:08:04.220526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
197541 80
 
< 0.1%
149753 80
 
< 0.1%
63458 77
 
< 0.1%
83993 76
 
< 0.1%
189951 76
 
< 0.1%
169647 75
 
< 0.1%
31611 74
 
< 0.1%
104741 74
 
< 0.1%
181991 72
 
< 0.1%
59321 72
 
< 0.1%
Other values (131199) 1383861
99.9%
ValueCountFrequency (%)
1 11
 
< 0.1%
2 31
< 0.1%
5 9
 
< 0.1%
7 9
 
< 0.1%
8 18
< 0.1%
9 22
< 0.1%
10 4
 
< 0.1%
13 5
 
< 0.1%
14 11
 
< 0.1%
17 6
 
< 0.1%
ValueCountFrequency (%)
206209 8
 
< 0.1%
206205 19
< 0.1%
206203 13
< 0.1%
206200 19
< 0.1%
206199 22
< 0.1%
206198 13
< 0.1%
206196 15
< 0.1%
206195 6
 
< 0.1%
206193 6
 
< 0.1%
206191 23
< 0.1%

eval_set
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.6 MiB
train
1384617 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters6923085
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtrain
2nd rowtrain
3rd rowtrain
4th rowtrain
5th rowtrain

Common Values

ValueCountFrequency (%)
train 1384617
100.0%

Length

2024-11-01T17:08:04.335560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-01T17:08:04.419901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
train 1384617
100.0%

Most occurring characters

ValueCountFrequency (%)
t 1384617
20.0%
r 1384617
20.0%
a 1384617
20.0%
i 1384617
20.0%
n 1384617
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6923085
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1384617
20.0%
r 1384617
20.0%
a 1384617
20.0%
i 1384617
20.0%
n 1384617
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6923085
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1384617
20.0%
r 1384617
20.0%
a 1384617
20.0%
i 1384617
20.0%
n 1384617
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6923085
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1384617
20.0%
r 1384617
20.0%
a 1384617
20.0%
i 1384617
20.0%
n 1384617
20.0%

order_number
Real number (ℝ)

Distinct97
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.09141
Minimum4
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 MiB
2024-11-01T17:08:04.520015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q16
median11
Q321
95-th percentile52
Maximum100
Range96
Interquartile range (IQR)15

Descriptive statistics

Standard deviation16.614037
Coefficient of variation (CV)0.97206939
Kurtosis5.8967139
Mean17.09141
Median Absolute Deviation (MAD)6
Skewness2.2433716
Sum23665057
Variance276.02621
MonotonicityNot monotonic
2024-11-01T17:08:04.652253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 149882
 
10.8%
5 123548
 
8.9%
6 105328
 
7.6%
7 90949
 
6.6%
8 75645
 
5.5%
9 68366
 
4.9%
10 60216
 
4.3%
11 51530
 
3.7%
12 47819
 
3.5%
13 42072
 
3.0%
Other values (87) 569262
41.1%
ValueCountFrequency (%)
4 149882
10.8%
5 123548
8.9%
6 105328
7.6%
7 90949
6.6%
8 75645
5.5%
9 68366
4.9%
10 60216
4.3%
11 51530
 
3.7%
12 47819
 
3.5%
13 42072
 
3.0%
ValueCountFrequency (%)
100 7624
0.6%
99 250
 
< 0.1%
98 292
 
< 0.1%
97 324
 
< 0.1%
96 469
 
< 0.1%
95 373
 
< 0.1%
94 431
 
< 0.1%
93 358
 
< 0.1%
92 416
 
< 0.1%
91 370
 
< 0.1%

order_dow
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7013918
Minimum0
Maximum6
Zeros324026
Zeros (%)23.4%
Negative0
Negative (%)0.0%
Memory size10.6 MiB
2024-11-01T17:08:04.752384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1676456
Coefficient of variation (CV)0.80241809
Kurtosis-1.3989458
Mean2.7013918
Median Absolute Deviation (MAD)2
Skewness0.1755159
Sum3740393
Variance4.6986876
MonotonicityNot monotonic
2024-11-01T17:08:04.847975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 324026
23.4%
6 207279
15.0%
1 205978
14.9%
5 176910
12.8%
2 160562
11.6%
4 155481
11.2%
3 154381
11.1%
ValueCountFrequency (%)
0 324026
23.4%
1 205978
14.9%
2 160562
11.6%
3 154381
11.1%
4 155481
11.2%
5 176910
12.8%
6 207279
15.0%
ValueCountFrequency (%)
6 207279
15.0%
5 176910
12.8%
4 155481
11.2%
3 154381
11.1%
2 160562
11.6%
1 205978
14.9%
0 324026
23.4%

order_hour_of_day
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.577592
Minimum0
Maximum23
Zeros9083
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size10.6 MiB
2024-11-01T17:08:05.047795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median14
Q317
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.238458
Coefficient of variation (CV)0.31216566
Kurtosis0.043845727
Mean13.577592
Median Absolute Deviation (MAD)3
Skewness-0.12102981
Sum18799765
Variance17.964526
MonotonicityNot monotonic
2024-11-01T17:08:05.149174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
14 119370
 
8.6%
15 116198
 
8.4%
13 114762
 
8.3%
11 114119
 
8.2%
12 111752
 
8.1%
10 110479
 
8.0%
16 110237
 
8.0%
17 96944
 
7.0%
9 93856
 
6.8%
18 76522
 
5.5%
Other values (14) 320378
23.1%
ValueCountFrequency (%)
0 9083
 
0.7%
1 5626
 
0.4%
2 3226
 
0.2%
3 2438
 
0.2%
4 2431
 
0.2%
5 3847
 
0.3%
6 11847
 
0.9%
7 36302
 
2.6%
8 67386
4.9%
9 93856
6.8%
ValueCountFrequency (%)
23 16965
 
1.2%
22 27319
 
2.0%
21 34813
 
2.5%
20 40920
 
3.0%
19 58175
4.2%
18 76522
5.5%
17 96944
7.0%
16 110237
8.0%
15 116198
8.4%
14 119370
8.6%

days_since_prior_order
Real number (ℝ)

Zeros 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.066126
Minimum0
Maximum30
Zeros17044
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size10.6 MiB
2024-11-01T17:08:05.249137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median15
Q330
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)23

Descriptive statistics

Standard deviation10.426418
Coefficient of variation (CV)0.61094228
Kurtosis-1.5712889
Mean17.066126
Median Absolute Deviation (MAD)9
Skewness0.074891246
Sum23630048
Variance108.71019
MonotonicityNot monotonic
2024-11-01T17:08:05.365016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
30 407265
29.4%
7 106801
 
7.7%
6 72138
 
5.2%
8 61821
 
4.5%
5 54117
 
3.9%
14 51690
 
3.7%
4 45727
 
3.3%
9 43410
 
3.1%
13 39081
 
2.8%
3 36550
 
2.6%
Other values (21) 466017
33.7%
ValueCountFrequency (%)
0 17044
 
1.2%
1 19265
 
1.4%
2 27504
 
2.0%
3 36550
 
2.6%
4 45727
3.3%
5 54117
3.9%
6 72138
5.2%
7 106801
7.7%
8 61821
4.5%
9 43410
3.1%
ValueCountFrequency (%)
30 407265
29.4%
29 15397
 
1.1%
28 21223
 
1.5%
27 15460
 
1.1%
26 12500
 
0.9%
25 14054
 
1.0%
24 13947
 
1.0%
23 15575
 
1.1%
22 20457
 
1.5%
21 29173
 
2.1%

Interactions

2024-11-01T17:07:59.334620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:45.883952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:47.753426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:49.684567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:51.685041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:53.767938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:55.620187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:57.521219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:59.584727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:46.118028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:47.984640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:49.917969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:51.918025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:54.016725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:55.867831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:57.734658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:59.834447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:46.351350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:48.234931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:50.167934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:52.300989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:54.251491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:56.084545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:57.984523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:08:00.084618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:46.588061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:48.468012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:50.401129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:52.567774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:54.484941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:56.334764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:58.222692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:08:00.320206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:46.817960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:48.706527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:50.651458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:52.817847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:54.684612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:56.568211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:58.434628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:08:00.567945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:47.051104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:48.934685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:50.867978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:53.069775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:54.918096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:56.784944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:58.651464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:08:00.834323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:47.284823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:49.184077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:51.118034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:53.301303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:55.167283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:57.003871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:58.884632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:08:01.067112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:47.504265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:49.433895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:51.351257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:53.534686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:55.384824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:57.284405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:07:59.118077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-01T17:08:05.449777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
add_to_cart_orderdays_since_prior_orderorder_doworder_hour_of_dayorder_idorder_numberproduct_idreordereduser_id
add_to_cart_order1.0000.018-0.024-0.0100.0020.0310.0070.137-0.000
days_since_prior_order0.0181.000-0.0250.0080.003-0.3870.0010.1660.004
order_dow-0.024-0.0251.0000.0090.0010.015-0.0040.017-0.006
order_hour_of_day-0.0100.0080.0091.000-0.003-0.0350.0020.034-0.001
order_id0.0020.0030.001-0.0031.0000.002-0.0010.004-0.001
order_number0.031-0.3870.015-0.0350.0021.000-0.0010.237-0.005
product_id0.0070.001-0.0040.002-0.001-0.0011.0000.042-0.001
reordered0.1370.1660.0170.0340.0040.2370.0421.0000.006
user_id-0.0000.004-0.006-0.001-0.001-0.005-0.0010.0061.000

Missing values

2024-11-01T17:08:01.332405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-01T17:08:01.851039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

order_idproduct_idadd_to_cart_orderreordereduser_ideval_setorder_numberorder_doworder_hour_of_daydays_since_prior_order
014930211112108train44109.0
111110921112108train44109.0
211024630112108train44109.0
314968340112108train44109.0
414363351112108train44109.0
511317660112108train44109.0
614720970112108train44109.0
712203581112108train44109.0
836396121079431train2361830.0
936196602179431train2361830.0
order_idproduct_idadd_to_cart_orderreordereduser_ideval_setorder_numberorder_doworder_hour_of_daydays_since_prior_order
138460734210583031661136952train2031815.0
138460834210583557870136952train2031815.0
138460934210583265081136952train2031815.0
138461034210634923511169679train300104.0
138461134210631356521169679train300104.0
138461234210631423331169679train300104.0
138461334210633554841169679train300104.0
138461434210703595111139822train156108.0
138461534210701695321139822train156108.0
13846163421070472431139822train156108.0